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Build Measure Learn

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ace3000chao
build-measure-learn

用开发—测量—认知反馈循环驱动产品迭代。当你想知道"做完产品后如何判断对不对"、"要不要坚持当前方向"、"什么时候该转型"时,使用此Skill。

Overview

Publisherace3000chao
Repositorybook2startup
Skill namebuild-measure-learn
Stars
93
Forks
14
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ace3000chao on GitHub. Read the source before you install it.

Installation

Install the Build Measure Learn AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ace3000chao/book2startup/tree/master/精益创业skills/skills/002-build-measure-learn
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Build Measure Learn in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Build Measure Learn on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Build Measure Learn is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Skill: Build-Measure-Learn循环

Metadata

  • ID: leanstartup-002
  • 类型: framework
  • 来源: 《精益创业》引言+第五章
  • 验证状态: ✅ 三重验证通过(V1: 全书核心, V2: 每日决策, V3: 非常识)

R — Reading(原文引用)

"新创企业的基本活动是把点子转化为产品,衡量顾客的反馈,然后认识到是应该改弦更张还是坚守不移。所有成功的新创企业的流程步骤都应该以加速这个反馈循环为宗旨。"

I — Interpretation(方法论骨架)

BML循环的核心洞察:创业不是一条直线(计划→执行→评估),而是一个不断加速的循环。

  • 开发(Build):不是"做完产品",而是"把想法转化为可以测试的形式"
  • 测量(Measure):不是"看数据涨跌",而是"确认我们获得了关于假设的真实反馈"
  • 认知(Learn):不是"学到了新东西",而是"证明或证伪了一个关键假设,决定坚持还是转型"

质量标准不是"功能多少",而是"这个循环转得多快"。

A1 — Past Application(书中案例)

IMVU的教训:作者团队花了6个月开发产品发布后,没有任何用户。但他们没有把"发布"当成终点,而是把"发布"当成循环的起点——每天追踪顾客行为、每周迭代,最终找到正确的产品方向。

Facebook的快速迭代:不是"想好所有功能再发布",而是每次只发布一个功能,观察数据,立刻决定是坚持还是调整。

A2 — Future Trigger(何时调用)

当你听到以下问题时,就应该调用这个Skill:

  • "产品发布后,下一步该做什么?"
  • "我怎么知道我们的方向是对的?"
  • "我们要不要做一个新功能/进入新市场/改变定价?"
  • "团队在争论该不该坚持当前方案,怎么裁决?"
  • "产品迭代了很多版本,但不确定是否真的在进步"

E — Execution(可执行步骤)

步骤1:确认当前循环的位置

问自己:这个循环现在卡在哪里?

  • 是不知道该开发什么?(想法太多,没有优先级)
  • 是开发了但没有测量反馈?(发布了就完事)
  • 是测了数据但不知道代表什么?(数据一堆,没有洞察)
  • 是知道方向错了但不敢调整?(缺乏转型勇气)

步骤2:为每个活动计时

开发时间:从想法到"能获取用户反馈的产品"要多久?

  • 目标:越短越好,目标≤1周
  • 如果超过1个月,问:"能否拆解成更小的可测试单元?"

测量时间:从发布到"获得有统计意义的数据"要多久?

  • 目标:越短越好,目标≤2周
  • 如果超过1个月,问:"是否有更快的验证方式?"

认知时间:从数据到"知道该坚持还是转型"要多久?

  • 目标:当天或次日决策
  • 如果超过1周,问:"数据是否清晰到可以决策?"

步骤3:问核心问题

"我们把时间投入哪个环节,能最大化加速整个循环?"

把资源集中在拖慢整个循环的那个环节。

步骤4:建立"加速"的定义

不要笼统说"加快迭代",要具体:

  • 从想法到用户反馈:原来X天 → 目标Y天
  • 从数据到决策:原来X天 → 目标Y天
  • 每月迭代次数:原来X次 → 目标Y次

B — Boundary(何时不适用)

不适用场景原因
基础研究/长期技术研发这类工作的反馈周期本身就很长,无法按"周"计算
高度监管的产品(药品/航空)合规要求限制了迭代速度
B2B大型复杂销售一个销售周期可能数月,不适合快速BML循环

作者盲点提醒:BML循环的"快"是有代价的——它要求团队习惯"不完美"并容忍频繁的方向调整。对于追求完美或权威导向的团队,这个循环会让成员焦虑。

关联Skills

  • MVP构建法 — MVP是BML循环中"开发"阶段的最佳实践
  • 转型决策树 — "Learn"阶段的产出决定是否需要转型
  • 三个"可"衡量指标 — 确保"Measure"阶段的数据是可执行的

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Build Measure Learn AI skill do?

用开发—测量—认知反馈循环驱动产品迭代。当你想知道"做完产品后如何判断对不对"、"要不要坚持当前方向"、"什么时候该转型"时,使用此Skill。

Why use Build Measure Learn on TypingMind?

Because you install it once and use it with any model. Build Measure Learn is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Build Measure Learn in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ace3000chao/book2startup/tree/master/精益创业skills/skills/002-build-measure-learn. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Build Measure Learn?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Build Measure Learn?

As many as you like. As long as a model supports skills, you can use Build Measure Learn with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Build Measure Learn AI skill free?

Yes. It is published on GitHub by ace3000chao under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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